World Unicorns 2019

World Unicorns 2019

USA obviously is ahead with 172 unicorns. China comes second with 89 unicorns. UK has 17, with India a close behind UK with 16 unicorns.

South Korea and Germany are next with 8 each. 9 countries have between 2 to 4 unicorns. 11 countries have at least 1 unicorn. In all 26 countries have unicorns.

How I did it?

World unicorns 2019

For mapping unicorns on the world map; I started with downloading the excel file from CB Insights.

The first challenge to me was the valuation was in currency format which was read as a string in pandas. By writing a function the valuation was converted into a number.

Then built-in group by function was used to group and aggregate on count of unicorns by country.

Now I did not have country codes in my data frame. The country codes were needed later to map it using pygal library. So, I downloaded country codes file from pygal. Later I mapped it and had my original data frame have country codes in an additional column. However, the mapping did not recognize South Korea. Its official name is different. So, I had to correct it.

Then the countries were put in five buckets as per the count of unicorns in a country.

The final challenge was to convert these five data frames into dictionaries. It was repeatedly giving type error. So, I had to go back to pandas and dictionaries and read again about sub setting pandas and converting to dictionaries. Finally, with a lot of tries I could convert five data frames in to five dictionaries.

These five dictionaries were used with pygal to plot the words map.